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Introduction: There remains no “gold standard” for the diagnosis of carpal tunnel syndrome (CTS). Clinical diagnosis is often held to be paramount but depends on the skills of the individual practitioner. We describe two mathematical approaches to the analysis of a history obtained by questionnaire. Methods: We used two earlier instruments, a conventional logistic regression analysis, and an artificial neural network to analyze data from 5860 patients referred for diagnosis of hand symptoms. We evaluated their ability to predict whether nerve conduction studies would show evidence of CTS using receiver operating characteristic curves. Results: Both new instruments outperformed the existing tools, achieving sensitivity of 88% and specificity of 50% in predicting abnormal median nerve conduction. When combined, 96% sensitivity and 50% specificity were achieved. Conclusion: The combined instrument can be used as a preliminary screening tool for CTS, for self-diagnosis, and as a supplement to diagnosis in primary care.
|Uncontrolled Keywords:||Science & Technology, Life Sciences & Biomedicine, Clinical Neurology, Neurosciences, Neurosciences & Neurology, carpal tunnel syndrome, diagnosis, nerve conduction studies, neural networks, questionnaire, LOGISTIC-REGRESSION, POPULATION, NERVE, WORK, VALIDATION, DISORDERS, SEVERITY, SYMPTOMS, OUTCOMES|
|Subjects:||R Medicine > RA Public aspects of medicine
R Medicine > RC Internal medicine
|Divisions:||School of Informatics > Centre for Health Informatics|
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